Structure and magnetic properties of ScFe6Ga6-type RCo5Ga7 (R = Y, Tb, Dy, Ho and Er)

The structure and magnetic properties of the RCo5Ga7 (R = Y, Tb, Dy, Ho and Er) compounds with the ScFe6Ga6-type structure have been studied. The stability of RCo5Ga7 is closely related with the ratio of the metal radii R-RE/R-(Co,R-Ga). With R-RE/R-(Co,R-Ga) less than or equal to 1.36, the compounds can be stabilized in the ScFe6Ga6-type structure. The lattice of RCo5Ga7 shrinks as the atomic order of R increases, and it is consistent with the lanthanide contraction. The structure analysis based on X-ray diffraction patterns reveals that in the orthorhombic RCo5Ga7 (Immm), R occupies the 2a site, and Co enters into the 8k and the 4h sites, and Ga is at the 4e, 4f, 4g, 4h and 8k sites. The interatomic distances and the coordination numbers of RCo5Ga7 are provided from the refinement results. The short interatomic distance (less than 2.480 Angstrom) between the Co ions results in the negative magnetic interaction, which does not favor ferromagnetic ordering. The magnetic moment of YCo5Ga7 is absent, and RCo5Ga7 (R = Tb, Dy, Ho and Er) may have long-range magnetic ordering with the paramagnetic Curie temperature lower than 5 K. (C) 2004 Elsevier Inc. All rights reserved.

Application of probabilistic neural network in the clinical diagnosis of cancers based on clinical chemistry data

As a recently developed and powerful classification tool, probabilistic neural network was used to distinguish cancer patients from healthy persons according to the levels of nucleosides in human urine. Two datasets (containing 32 and 50 patterns, respectively) were investigated and the total consistency rate obtained was 100% for dataset 1 and 94% for dataset 2. To evaluate the performance of probabilistic neural network, linear discriminant analysis and learning vector quantization network, were also applied to the classification problem. The results showed that the predictive ability of the probabilistic neural network is stronger than the others in this study. Moreover, the recognition rate for dataset 2 can achieve to 100% if combining, these three methods together, which indicated the promising potential of clinical diagnosis by combining different methods. (C) 2002 Elsevier Science B.V. All rights reserved.